Identifying model inaccuracies and solution uncertainties in noninvasive activation-based imaging of cardiac excitation using convex relaxation. Erem, B., van Dam, P., & Brooks, D. IEEE Trans Med Imaging, 33(4):902–912, Apr, 2014.
bibtex   
@Article{RSM:Ere2014,
  author =       "B. Erem and P.M. van Dam and D.H. Brooks",
  title =        "Identifying model inaccuracies and solution uncertainties
                 in noninvasive activation-based imaging of cardiac
                 excitation using convex relaxation.",
  journal =      "IEEE Trans Med Imaging",
  year =         "2014",
  month =        "Apr",
  volume =       "33",
  number =       "4",
  pages =        "902--912",
  robnote =      "Noninvasive imaging of cardiac electrical function has
                 begun to move towards clinical adoption. Here, we consider
                 one common formulation of the problem, in which the goal
                 is to estimate the spatial distribution of electrical
                 activation times during a cardiac cycle. We address the
                 challenge of understanding the robustness and uncertainty
                 of solutions to this formulation. This formulation poses a
                 nonconvex, nonlinear least squares optimization problem.
                 We show that it can be relaxed to be convex, at the cost
                 of some degree of physiological realism of the solution
                 set, and that this relaxation can be used as a framework
                 to study model inaccuracy and solution uncertainty. We
                 present two examples, one using data from a healthy human
                 subject and the other synthesized with the ECGSIM software
                 package. In the first case, we consider uncertainty in the
                 initial guess and regularization parameter. In the second
                 case, we mimic the presence of an ischemic zone in the
                 heart in a way which violates a model assumption. We show
                 that the convex relaxation allows understanding of spatial
                 distribution of parameter sensitivity in the first case,
                 and identification of model violation in the second.",
  bibdate =      "Sun Apr 10 19:47:10 2016",
  pmcid =        "PMC3982205",
}

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